One of the most common metrics used to measure the forecasting accuracy of a model is **MAPE**, which stands for **mean absolute percentage error**.

The formula to calculate MAPE is as follows:

**MAPE** = (1/n) * Σ(|actual – forecast| / |actual|) * 100

where:

**Σ**– a fancy symbol that means “sum”**n**– sample size**actual**– the actual data value**forecast**– the forecasted data value

MAPE is commonly used because it’s easy to interpret and easy to explain. For example, a MAPE value of 11.5% means that the average difference between the forecasted value and the actual value is 11.5%.

The lower the value for MAPE, the better a model is able to forecast values. For example, a model with a MAPE of 2% is more accurate than a model with a MAPE of 10%.

**How to Calculate MAPE in Excel**

To calculate MAPE in Excel, we can perform the following steps:

**Step 1: Enter the actual values and forecasted values in two separate columns.**

**Step 2: Calculate the absolute percent error for each row.**

Recall that the absolute percent error is calculated as: |actual-forecast| / |actual| * 100. We will use this formula to calculate the absolute percent error for each row.

Column D displays the absolute percent error and Column E shows the formula we used:

We will repeat this formula for each row:

**Step 3: Calculate the mean absolute percent error.**

Calculate MAPE by simply finding the average of the values in column D:

The MAPE of this model turns out to be **6.47%**.

**A Note On Using MAPE**

Although MAPE is straightforward to calculate and easy to interpret, there are a couple potential drawbacks to using it:

**1. **Since the formula to calculate absolute percent error is |actual-forecast| / |actual| this means that it will be undefined if any of the actual values are zero.

**2. **MAPE should not be used with low volume data. For example, if the actual demand for some item is 2 and the forecast is 1, the value for the absolute percent error will be |2-1| / |2| = 50%, which makes it seem like the forecast error is quite high, despite the forecast only being off by one unit.

*Another common way to measure the forecasting accuracy of a model is MAD – mean absolute deviation. Read about how to calculate MAD in Excel here.*